33 citations · 49 across the 6 of their papers we have counts for
7 papers
Magnet-Aware Control of Legged Robots
J. Playan Garai, S. B. Djuve, C. McGreavy +1
Autonomous robots can increase uptime and reduce human exposure in Big Science facilities, but strong magnetic fields needed for their operation corrupt sensors and induce pose-dep…
Identifying Important Sensory Feedback for Learning Locomotion Skills
Wanming Yu, Chuanyu Yang, Christopher McGreavy +5
Robot motor skills can be learned through deep reinforcement learning (DRL) by neural networks as state-action mappings. While the selection of state observations is crucial, there…
Metrics for 3D Object Pointing and Manipulation in Virtual Reality
Eleftherios Triantafyllidis, Wenbin Hu, Christopher McGreavy +1
Assessing the performance of human movements during teleoperation and virtual reality is a challenging problem, particularly in 3D space due to complex spatial settings. Despite th…
Reachability Map for Diverse Balancing Strategies and Energy Efficient Stepping of Humanoids
Christopher McGreavy, Zhibin Li
In legged locomotion, the relationship between different gait behaviors and energy consumption must consider the full-body dynamics and the robot control as a whole, which cannot b…
Multimodal Interfaces for Effective Teleoperation
Eleftherios Triantafyllidis, Christopher McGreavy, Jiacheng Gu +1
Research in multi-modal interfaces aims to provide solutions to immersion and increase overall human performance. A promising direction is combining auditory, visual and haptic int…
Optimisation of Body-ground Contact for Augmenting Whole-Body Loco-manipulation of Quadruped Robots
Wouter Wolfslag, Christopher McGreavy, Guiyang Xin +3
Legged robots have great potential to perform loco-manipulation tasks, yet it is challenging to keep the robot balanced while it interacts with the environment. In this paper we st…
Recurrent Deterministic Policy Gradient Method for Bipedal Locomotion on Rough Terrain Challenge
Doo Re Song, Chuanyu Yang, Christopher McGreavy +1
This paper presents a deep learning framework that is capable of solving partially observable locomotion tasks based on our novel interpretation of Recurrent Deterministic Policy G…